Data Warehousing

ITEM TITLE
1) Data Warehousing Introduction 2) Data Warehouses At a Glance 3) Data Warehouse Overview 4) Data Warehouse Tools 5) Data Warehousing Methods 6) Data Warehouse Design Strategies 7) How To Assess Your Data Warehouse 8) How To Create a Data Warehouse Structure 9) How To Protect Your Data Warehouse 10) How To Manage Current and Historical Information Within Your Data Warehouse 11) How To Use Data Warehouses Strategically 12) Maintaining Records Within a Data Warehouse 13) Advantages and Disadvantages to Using a Data Warehouse 14) The Difference Between Data Mart and Data Warehouse 15) How To Properly Manage a Data Warehouse 16) How To Manage Meta Data Within a Data Warehouse 17) Federated Data Warehouse Architecture 18) Historical Information About Data Warehouses 19) Data Warehouse Business Principles 20) Understanding The Data Warehouse 21) The Benefits of Data Warehouses 22) The Disadvantages of a Data Warehouse 23) Rules to Use With Your Data Warehouse 24) Data Warehouse Issues 25) Crucial Requirements For Successful Data Warehouses 26) Why Data Warehouses Can Be Useful 27) Fundamental Themes For Your Data Warehouse 28) What You Should Know About Building a Data Warehouse 29) How To Rate Your Data Warehouse 30) How Data Is Stored Within a Data Warehouse 31) How Does a Data Warehouse Differ From a Database 32) Creating an Efficient Process for Data Warehouses 33) Understanding Quality Management For Data Warehouses 34) How To Evaluate The Software For your Data Warehouse 35) Understanding The Challenges of Using Data Warehouses

Data Warehousing Introduction
A data warehouse is a type of computer database that is responsible for collecting and storing the information of a particular organization. The goal of using a data warehouse is to have an efficient way of managing information and analyzing data. Despite the fact that data warehouses can be designed in a number of different ways, they all share a number of important characteristics. Most data warehouses are subject oriented. This means that the information that is in the data warehouse is stored in a way that allows it to be connected to objects or events which occur in reality. Another characteristic that is frequently seen in data warehouses is called a time variant. A time variant will allow changes in the information to be monitored and recorded over time. The information that exists in data warehouses is non-volatile. This means that it cannot be deleted, and must be held to be analyzed in the future. All of the programs that are used by a particular institution will be stored in the data warehouse, and it will be integrated together. The first data warehouses were developed in the 1980s. As societies entered the information age, there was a large demand for efficient methods of storing information. Many of the systems that existed in the 1980s were not powerful enough to store and manage large amounts of data. There were a number of reasons for this. The systems that existed at the time took too long to report and process information. Many of these systems were not designed to analyze or report information. In addition to this, the computer programs that were necessary for reporting information were both costly and slow. To solve these problems, companies begin designing computer databases that placed an emphasis on managing and analyzing information. These were the first data warehouses, and they could obtain data from a variety of different sources, and some of these include personal computers and mainframes. Spreadsheet programs have also played an important role in the development of data warehouses. By the end of the 1990s, the technology had greatly advanced, and was much lower in cost. The technology has continued to evolve to meet the demands of those who are looking for more functions and speed. There are four advances in data warehouse technology that has allowed it to evolve. These advances are offline operational databases, real time data warehouses, offline data warehouses, and the integrated data warehouses. The offline operational database is a system in which the information within the database of an operational system is copied to a server that is offline. When this is done, the operational system will perform at a much higher level. As the name implies, a real time data warehouse system will be updated every time an event occurs. For example, if a customer orders a product, a real time data warehouse will automatically update the information in real time. The offline data warehouse is a database that is updated on a regular from an operational system.

Wih the integrated data warehouse, transactions will be transferred back to the operational systems each day, and this will allow the data to easily be analyzed by companies and organizations. There are a number of devices that will be present in the typical data warehouse. Some of these devices are the source data layer, reporting layer, data warehouse layer, and transformation layer. There are a number different data sources for data warehouses. Some popular forms of data sources are Teradata, Oracle database, or Microsoft SQL Server. Another important concept that is related to data warehouses is called data transformation. As the name suggests, data transformation is a process in which information transferred from specific sources is cleaned and loaded into a repository. Data transformation can either be a manual or automated process. Code can be manually generated, or an ETL tool can be utilized. The device that is responsible for transforming the data will compare it to other systems. It will also placed the data in a specific standard. In addition to this, it will often be linked to other systems which can assist it. The goal of using a data warehouse is to store and monitor information in a way that allows it to easily tbe analyzed. The data held in the warehouse will typically remain on file for a year.

Data Warehouses At a Glance
Data warehouses have played an important role in information technology since the 1990s. They are tools that allow organizations to use relevant information to make important business decisions. While data warehouses were originally only used by large companies, the decreasing cost of computing has allowed them to be adapted by smaller companies. To understand data warehouses, it is important to learn about the data warehouse architecture. Data warehouses are computerized systems that store information. They information will almost always come from another source. These sources could be programs or applications. Not only is the data warehouse able to store information, it can also analyze that information as well. This is what separates a data warehouse from being a mere computer storage device. Managers can search through the warehouse looking for specific information. Data mining programs can be initiated, and they will look through the data warehouse for patterns or relationships that can help companies make important marketing decisions. A data warehouse is basically a database that can answer certain questions. They are subject oriented, and they will analyze information and can help managers solve problems. There are a number of steps that are involved with building a data warehouse. These procedures are similar to those that would be used to build other computer programs. The users of the data warehouse must play a role in its construction. The user is important because they are the people who will be using the tools. Each data

warehouse is different, and it must be designed in a way that will allow it to meet the needs of those who use it. The users will decide what type of information will be placed in the warehouse. Once the requirements for the data warehouse have been developed, the elements must be placed in a conceptual model. This will act as a diagram that will be used to build the actual database. At this point, there are a large number of decisions which need to be made about the design and implementation of the warehouse. Once the warehouse has been built, the data must be acquired and stored. It is up to the data warehouse managers to decide what information must be stored in the database. Much of this data will be related to the organization that owns the data warehouse. However, some of the data may be taken from other sources. An extraction application must be created in order to pull data from other sources to be placed in the data warehouse. The sources must be identified, and some of these will be file, legacy systems, or other databases. The information will be copied into what is called a staging area. Once the data is placed in the staging area, it will need to be cleaned. Once it is clean and free of errors, it will then be copied into the warehouse. It is crucially important to make sure the data is moved into the warehouse correctly. It is not, the project will not be successful. Metadata is another important concept that is connected to data warehouses. In fact, high quality meta data is important for the function of the database. Metadata is information about information. It is used in the information collection process, and it is also used when the data is accessed or transformed. In the acquisition phase, the information will be mapped and transferred from the operational system. I will provide a large amount of information about the data, and some of this includes updates or algorithms. Data marts are also important. While managers will want to keep them updated, they don't need to be updated in real time. Data marts are small in comparison to data warehouses and are only hold information about departments that exist within an organization. Many companies have begin combining a number of small data marts in order create a data warehouse. However, this has led to controversy. Some feel that data marts where never designed to function as data warehouses, and they should not be used for this purpose. It is best to use data marts as a component to a data warehouse instead of a standalone entity. Security is an important issue with data warehouses, and the information must be protected.

Data Warehouse Overview
The word data warehouse was first developed by Bill Inmon in the early 1990s. He referred to it as being a integrated collection of information that could help companies and organizations make better decisions. To be effective, a data warehouse had to be integrated, subject oriented, nonvolatile, and time variant. In this article, I will go over all these factors in detail. If you

it will allow you to analyze information that is connected to a specific subject. it is important for you to understand why they are important. Data acquisition can be defined as transferring data from a source to the warehouse. many companies are forced to delete some of their information after a certain period of time. and there are a number of products currently be sold to deal with it. The historical information may be stored in a legacy system. and is challenging to extract. Once the initial data has been transferred to the data warehouse. Data acquisition is one of the most expensive parts of building a data warehouse. some companies will systematically delete data that has reached three years of age. Another important aspect of data warehouses is data acquisition. but can be combined into one unit that is relevant and logical. it should be noted that some data warehouses are volatile. As of this time. It is important for data to be cleaned before it can be placed in the warehouse. It is important that the information contained within a data warehouse is stable. the process must be repeated consistently. Data acquisition is a continous process. the correct data must be located. For instance. When a company uses a data warehouse that is stable. it should never be deleted. the information that will be added to the warehouse will come from daily information or historical information.are building a data warehouse. This property is referred to as being nonvolatile. it is important for designers to understand the database schema. Generally. Being subject oriented means that the data will provide information about a specific subject rather than the information about the functions of a company. However. Being integrated means that the data that is collected within the data warehouse can come from different sources. there are just over 50 ETL tools being sold. The design of the data warehouse is important as well. While data can be added. This process has become a separate field. It may cost a company millions of dollars in order to transfer data from sources to the warehouse. This process will often be conducted with an ETL tool. Because a data warehouse is subject oriented. they are still highly accurate today. this will allow them to get a better understanding of the operations within their company. The reason for this is because many modern data warehouses deal with terabytes of data. Having a time-variant means that all the information within the data warehouse can be found with a given period of time. Because they must store terabytes of data. as it is difficult to recreate some forms of data. When the warehouse is updated. To do this successfully. and the goal of a company is to make sure the warehouse is updated on a regular basis. Despite the fact that these terms were first coined in the the 1990s. It is important for designers to make sure the design is consistent with the queries that will be conducted within the warehouse. Before a data warehouse can be built. The data
. The process of dealing with this issue is called changed data capture. It is crucial to make sure the data warehouse is designed correctly. it is often hard to determine which information in the source has changed since the previous update.

and a company may make incorrect decisions based on it. the cost for this can be huge depending on the storage options you choose. and On-Line Analytical Processing. These are tools that will allow a user to view data from a wide variety of angles. and it cannot be used.
Data Warehouse Tools
There are a number of important tools which are connected to data warehouses. Business intelligence can further be broken down into a field that is called multi-dimensional analysis tools. you can store data based on each transaction. It is comprised of things such as Executive Information Systems. Another tool that is connected to data warehouses is data visualization. Once you have carried out an operation. or Virtual Reality Modeling language. the queries will move at a much faster rate. Before you decide which one you will use. The best way to handle this situation is to make sure the data warehouse is constructed with a large amount of detail. A query tool will allow a user to send SQL queries within a warehouse to look for results. Decision Support Systems. In order for data warehouses to function properly. you will need to rebuild the warehouse in order to undo it. This data could come in the form of intricate 3D images. and will allow you to look for patterns and relationships within a data warehouse. and this information may be important for solving a certain problem. A data warehouse can be designed to store information based on a certain level of detail. Business intelligence is a field which is very diverse." even though they mean the same thing. However. all the information within a data warehouse that means the same thing must be stored in the same form. However. The tools that can allow you to do this will fall under a topic that is called business intelligence. These are examples of data aggregation. only one of them can be used to recognize the element within the data warehouse. The goal of data visualization is to allow the user to view trends in a method which is easier to understand than complicated models that are based off statistics. When data is summarized.cleansing process is usually done during the data acquisition phase. This could lead to a number of problems. The reason for this is because the data may not be correct if it is not cleaned. For example. or you can store it based on a summary. If there is information that reads "MS" and "Microsoft. you will want to use this data to help you make smart investment decisions. Any data that is placed in a warehouse before being clean will pose a danger to the system. Once you have filled your data warehouse with important information. For example. some of the information may be lost during a query. it is also important
. The tools that are used for data visualization will present visual models of data. and one of these is data aggregation. it is important to weigh your options carefully. One tool that is allowing this field to advance is VRML. Data mining is also a field that falls under business intelligence.

" Meta data must be managed when data is acquired or analyzed. While many organizations agree on the overall goal of data warehouses. Meta data will be held in a repository. and you will run into a number of long term problems. If it is done properly. it will be sent to the presentation area. you will be able to discover important patterns that you didn't know existed. attempting to use data marts alone will be inefficient.
Data Warehousing Methods
Most organizations agree that data warehouses are a useful tool. Data warehousing is a field which is somewhat complicated. Once you understand and acquire the technology. The reason why it is important is because it can allow organizations to analyze the changes that occur within database tables. The process of properly managing meta data has become a science within itself. There are many vendors who are attempting to advertise the tools. In addition to this. Any company that is thinking of using data warehouses must make sure they have taken the time to review and understand the technology. but the cost and complexity involved with the products has not allowed them to be used by a large number of companies. the approaches to building them may differ. This has made data warehouses attractive to many companies. the company can greatly benefit. With techniques such as data mining and data visualization. and once the data has been prepared. Not only can you improve the marketing strategies. but you will also be able to make strategic decisions based on the information you have compiled and organized.to place an emphasis on metadata management. It is also important for them to make sure the correct information is published. Attempting to use data marts alone is not a good approach. and these are presentation and staging. it is possible for you to gain a powerful advantage over your competitors. There are two techniques for building data warehouses that have become very popular. One of the biggest advantages to data warehouses is that they allow you to store information that you can use to improve the marketing strategies of your company. It can only be useful if you know how to use it. because they are geared towards departments. It is composed of ETL operations. This is a tool that plays an important part of the construction of a data warehouse. There are two elements that make up the data warehouse environment. a number of programs will analyze and review it. They benefit from the ability to store and analyze data. These are the Kimball Bus Architecture and the Corporate
. and it should be easy to access by the people who are responsible for making decisions. When the data is placed within the presentation area. and can give you important information about many of the data warehouse tools. and this can allow them to make sound business decisions. Meta data can be described as being "information about information. The patterns that you discover can allow your company to earn large profits. The staging could also be known as the acquisition area.

others will be distributed. and this model will hold the same information as a standard model. there are some notable differences as well. the Kimball method states that the atomic data should be placed within a dimensional structure. It is important to make sure the information you have is detailed so that users will be able to ask relevant questions. However. During the staging process. One of the primary differences between these two techniques is the normalized data foundation. and it will display information that is summarized. With the Kimball technique. In most cases. When the data is placed within a dimensional structure. While there are some similarities between these to techniques. the system will become highly efficient. When business processes have been developed within the warehouse. The data will be populated once it is placed within the dimensional warehouse. Another name for this technique is the EDW approach. The next popular data warehouse approach that you will want to become familiar with is the Corporate Information Factory. The data marts may be designed for specific departments. While some of the staging processes may be centralized. Many of the people who choose to use a normalized data structure believe that it is faster than the dimensional structure. Another thing that separates the two data warehouse approaches is the management of atomic data. The approach that you choose should be the one which best serves the needs of your company. With the CIF. the duplicate storage of data is not required in both dimensional and normalized foundations. it can be summarized in a wide variety of different ways. It is important to make sure the data is properly handled during this step. the rough data will be transformed and refined within the staging area. The presentation area will have a dimensional structure. The atomic data may be obtained from the standard data warehouse. Departments within the organization do not play a role in this. a standard data warehouse is used to hold data repositories. A dimensional model will be created by a business operation.Information Factory. It is important for them to have the details so that they will be able to answer important questions. and they may have summary data which is in the form of a dimensional structure. and is not dependent on the various departments that may compose an organization. the rough data will be pulled from the source systems. the data structures that must be obtained before the dimensional presentation will be dependent on the source data and transformation.
. but they often fail to take ETL into consideration. In contrast. and it may also have specific data warehouses which are designed for data mining. they may want a summary of a large number of transactions. With the Kimball approach. The data that is extracted from the source will be coordinated. atomic data will be stored within a normalized data warehouse. it will be easier to use. While most users will not place an emphasis on the details of one atomic transaction. Within the CIF.

The reason for this is because end users will not utilize a program that is too difficult to use. you will want to next focus on data integrity. Design it in a way which will allow it to support expansions or upgrades. This is a concept that is an important part of your business. The best data warehouses are those which are scalable. The data warehouse that you design should fall under the guidelines of information
. Once you have designed a data warehouse that is user friendly. This basically means that you will want to design at system that is simple to use. When errors or technical problems do occur. You will want to keep these costs low as much as possible. The goal of your organization should be to integrate data and create standards that will be used and followed. When it comes to creating a data warehouse. First off. Once the data warehouse has been created. Implementation efficiency is a principle that naturally leads to the next topic you will want to focus on. After data integrity. they should be simple to fix. there are a number of IT design principles that you will want to follow. it will slow down the speed and productivity of your operation.Data Warehouse Design Strategies
To build an effective data warehouse. You will want to avoid designing a data warehouse that will load data that is not consistent. and this could cause conflicts. You should be able to adapt it to a number of different business situations. After you have got your colleagues behind the concept of using a data warehouse. If workers feel that a data warehouse is unnecessary. you can run into a number of different problems. Everyone in your organization should understand the importance of using the system. Use a design which is friendly and easy to learn. In addition to this. This is a problem that many data warehouse designers run into. Another thing you will want to look at is the cost involved with supporting the system. The design principles that have been discussed in this article so far are more related to business than information technology. It is also important to avoid creating a database that will replicate data. you will next want to look at operational efficiency. The proper methods for building a powerful data warehouse are based on information technology tactics. it is important that you and your organization understand the importance of having a data warehouse. It is important for you to keep them in mind. and this is user friendliness. It doesn't matter how well designed your data warehouse is if your workers have a hard time using it. it is important for you to understand data warehouse design principles. they may not use it. All of your workers should be able to use it without problems. you will want to make it as simple as possible. One of these is scalability. If your workers have a hard time using the data warehouse. it should not have errors or other technical problems. The best way to deal with this issue is to create a data warehouse that is scalable from the beginning. you will next want to look at implementation efficiency. If your data warehouse is not built correctly. it should be able to carry out operations quickly. However.

The terms that you will need to deal with when you assess your data warehouse is "how. It is not simply enough to acquire a data warehouse.
How To Assess Your Data Warehouse
While many large companies now use data warehouses. Second. and how you use them will play a powerful role in whether you succeed or fail. Every tool that you use to build your data warehouse should work well with IT standards. One reason for this is the difficulty that is often involved with data warehouses. and analyze information in order to make strategic decisions." and "what. While following the guidelines in this article won't allow you to always be successful. and the tools have continued to change on a consistent basis. It is matter of "how" and "when. This is where assessing your data warehouse is so important." "why. Any company with sufficient resources can do this. You will be able to find mistakes that you can avoid in the future. There are a number of techniques that must be used in order to identify and extract data. The success of your company lies in its ability to produce powerful processes which can be used to achieve the best results. First. and must be able to record. it is not a matter of "if" you will run into problems. No matter which process you develop for your data warehouse. It is these two issues that companies will want to pay attention to. But before you can begin assessing your data warehouse. it will be difficult for companies to meet these challenges if they are not capable of properly using their data warehouses. The principles and methods which are used to manage data warehouses have not been developed. it will greatly tip the odds in your favor. Despite these problems. and you will also be able to find successful methods that can be used again. you will want to review and find warehouse processes that were successful and use them to your advantage. it often requires a large amount of technical skill in order to manage data warehouses." When your data warehouse is well designed. However. However. you will be better equipped to solve any problems you encounter. The first step in properly using your data warehouse is to develop powerful business processes and methods. Data warehouses are tools. there is a large demand for information management systems. No matter how well designed your data warehouse is. You should be wary of companies that promise you perfect results if you use their design methods. there are a number of things you will want to keep in mind.technology standards. Many of these complications have caused a number of data warehouse programs to fail. When it comes to using a data warehouse. You will want to make sure it is designed in a way that makes it easier for your workers to use. monitor." The goal of looking at these terms is to find the best processes and methods which will allow you and your company to prosper. you will want to avoid making the same mistakes over again. Many companies use data warehouses because they are faced with powerful competition. Because of this. you will need to
. you will always run into problems. following the right principles will make the problems easier to recognize and solve. the concept has not yet become fully mature.

and you may decide to place an emphasis on knowledge management. If you are about to use your data warehouse for the first time. there will be no connections. However. or specifications? Would you want to buy such a house? It is likely that you would not want to purchase such a home. and you should also know how your data warehouse can help you care for these needs. As an example. It must be done whenever it is necessary. When your data warehouse does not have an organized structure. You will need to use a
.know when you should assess it. your needs will change. The structure will show how these components work together. If you want your company to compete successfully in your industry. As you can imagine. You should also determine if your organization is ready to use the data warehouse after they have build it. you will be able to make good decisions that can allow you company to succeed. you or your company may decide that the data warehouse should become the central point in your operation. The best time to assess your data warehouse is when you are not certain which direction your company should go in. You should know the needs of your business. Time is money. it is the data warehouses with the best structures that are the most likely to succeed. you will need to design a data warehouse structure that is highly organized and efficient. and it may also show you how the database will grow over a given period of time. and the database will be difficult to maintain. The same principle can be applied to designing a data warehouse. In fact. While every data warehouse will have a structure. As technology continues to advance. maps. and you don't want to waste time assessing the warehouse if it is not necessary. while others or not. Not only would it be uncomfortable. it is not enough to assess the data warehouse once. When you are able to properly assess your data warehouse. As your company continues to grow. you will want to assess the data warehouse periodically to find out which areas need to be upgraded. this is an example of a time when you will want to assess it. It should also be assessed if you notice that it is lagging in certain areas. Assessing your warehouse is not something that can only be done once. some are highly organized. but it could also be dangerous. The information that you gain from an assessment will allow you to make better decisions about how the data warehouse should be used. it will not be as flexible as it should be.
How To Create a Data Warehouse Structure
A data warehouse structure can be defined as the elements and components which make up the database. Another time to assess your data warehouse is when your company is running into problems. how well do you think a house would be built of the architects failed to use blue prints. If it doesn't have a structure. and the data warehouse will need to be reassessed.

this will need to be built into the structure. you will need to have a technical understanding of the system. The most important factors that are connected to infrastructure architecture are flexibility. data sources. They should also be maintained in the same way. It should be cleaned. you will want to pay attention to the data sources. It is also important to look at the components that will make up the structure. the software you use should be easy to transfer between the machines. there are no standards that have been developed. and this data may need to be encrypted or compressed. In addition to this. and scalability.
An emphasis should also be placed on the technical architecture. Because the design of a data warehouses is a new concept. The first important thing to realize is that the structure of your data warehouse should be directly connected to your business.
How To Protect Your Data Warehouse
. and you should know how the system will grow as it is used. you may need to create a structure that provides information that is related to shipping or billing. In fact. Once you have made your decision. All of the components should be designed. There should be a sufficient amount of bandwidth available to transfer information. The two primary components that will make up your data warehouse structure are technical elements and data elements. hardware. However. and daily updates. It should use a process which is based off a meta data catalog. The data that exists within the data warehouse must have the same structure."blue print" to make sure it is designed correctly. integrated. The desktop computers that you use should be powerful enough to run the necessary software programs. The one you choose is dependent on how your organization operates. It is these things which currently make data warehouses challenging to build. You cannot afford to build a database that has a structure which is disorganized. A common issue that is raised among organizations is the decision of whether they show data as dimensional or entity/relationship. To build this function into the structure. You will also want to look at business operations. you will next want to look at infrastructure architecture. and audited. The data warehouse should be able to pull data from numerous sources. networking. dependability. you shouldn't have a hard time building this. customer analysis. You will also want to make sure the data is transformed properly. if you need to design a data warehouse that will update each night. Some of the things that you may want to build within your data warehouse structure are global availability. It is also important to make sure the loading is conducted on numerous targets. it is crucial that you build them with the right structure. When it comes to the network. and operating systems. When it comes to the data. size. If you do your research. For example. the terminology which is related data warehouses is still being developed.

the financial information within a data warehouse will need to have a different level of security than the information that is related to inventory. and these are analytical. Generally. When you set up a security system for your data warehouse. there are also some disadvantages as well. you will want to set up a security system that can accomodate it. It is especially important during the planning stages. some companies wish to place limits on the type of information that a worker can access. There will only be a handful of people in most companies that will know how to make plans based on the information they obtained in a analytical warehouse. Despite this. After the application. Placing a security system within an application is efficient because it can be connected to the data that is being processed by the application. The reason for this is because it is this area of the warehouse that will have the most activity. there are three areas you will want to pay attention to. standardized reporting is a different issue. the next best place to add security is the data warehouse itself. While these people have a tremendous amount of skill and knowledge. In addition to this. You may also want to use a database level
. In order for you to secure your data warehouse. different departments within a company may have their own levels of security. When security is added to the data warehouse.While many data warehouses are used to access and analyze information. they only comprise a small part of those working with data warehouses. With standardized reporting. and it is the most vulnerable to performance problems. As you can imagine. In addition to this. the actual functions of the program can be secured as well. it is important to make sure you don't add too much. over 70 percent of data warehouses are built with standardized reporting in mind. However. If most of the people who use the data warehouse will only be looking at basic reports. A data warehouse that places an emphasis on consolidation seeks to integrate the information that it contains. The one that you choose will be dependent on a number of factors. a security system is not an option. The two places which are commonly chosen are the database level and application. If you are using more than one program within the database. While you will want to take some security measures. you will need to decide where the security system should be added. Despite this. and standardized reporting. When multiple sources of information are brought together. it is first important to understand what function your data warehouse is being designed for. some companies may choose to merge the data into a single source. all the computer programs will be secure. The analytical aspect of data warehouses is the thing you will hear about the most. While there are advantages to doing this. Trying to use advanced security measures in an analytical data warehouse is generally worthless. security will become a complex issue. it may be best to use a database level security program. a company will not place an emphasis on security until after the data warehouse has been built. it is important to make sure that each form of information has its own security system. If you are thinking of adding security to your data warehouse. consolidation. Before you set up a security system for your data warehouse.

and one example of this is slowly changing dimensions. Another thing that you will want to become familiar with is the security table. which can come in type 2 or 3.
How To Manage Current and Historical Information Within Your Data Warehouse
In order for a company to use a data warehouse successfully. The SCD 2 method will use keys within the table that will not change. However. the security table can play an important role in making sure your information is secure. The goal of using SQL is to produce facts which are grouped together over time. Unfortunately. The second common element that is used to generate historical information is called SQL. and can use the keys from both tables. or Structured query language. The SCD 2 is a technique which will solve part of the problem. and they will have an effect on the data model design and ETL functions. a new key will be added to the table. As an example. There are a number of basic modeling techniques that are used. You may find that the security table can become the largest table within the warehouse. One of the biggest challenges that data warehouse managers face today is the issue of how to manage dimensional tables over a given period of time. but they must also be able to manage this information with current data. There are a number of things that will result from this technique. Whenever a change occurs. a finance company may need to analyze their profits for the last three years. The queries that are run within the table will generate the correct historical views. The information is grouped based on iterations and the keys that the dimensions have moved
. The table will hold values which are related to the information that the user is allowed to access. because it will add historical information into the dimension. The SCD 2 technique can be utilized to display a dimensional table when a change within similar columns needs to be analyzed over a given period of time. The security table will contain characteristics that are secured along with the identification of the user. only having a current view will not allow the user to get the information they need.security system if more than 100 users will be accessing the data warehouse. The speed of your data warehouse may also be slowed. Looking at the data from the last three years will allow the user to view the transformations the company has gone through. it must be designed so that users are able to analyze historical and current information. the new keys can be tagged as current while the records which were loaded in the past can be tagged as being historical. If meta data columns were added to the structure of the table. Not only must they be able to do this. When you secure your data warehouse. it is important to make sure the right levels of security are set up. Each type of information within the database will need to be secured in a different way.

and they have still not been able to use it successfully. The fact table will be connected to the historical keys. One of the most important aspects of data warehouses is time. There are a number of ways you can use a data warehouse in order to make important decisions. you must learn how to use it strategically.through. When it comes to a future.
. and this solves the problem of using multiple keys which is found when using SCD 2. As the same time. and one will deal with current facts while the other will deal with historical facts. With this technique. It will use two columns within the table. and while one of them will be for current data. When it comes to the present. companies must be able to analyze information related to current issues so that they will be able to deal with them. Once you purchase or build the data warehouse. you will simply manage two sets of tables. It may also not be useful when all the facts that must be displayed must be connected to current information. Intricate SQL techniques can be utilized in order to gain the data on the current row.
How To Use Data Warehouses Strategically
It is not enough for a company to simply acquire a data warehouse. SCD 3 can model a dimension table to grab the current and historical changes which have been made on a key. you will need a tool that can use the dimension tables that will be used to issue reports. A company can analyze the information within their data warehouse to learn about the mistakes they made in the past. The meta data can find the rows in the table and ignore certain types of data. and future. The use of the meta data will also play a role in its success. Specifically. Another method that is used to balance historical and current data is SCD 3. Another technique that is used to manage historical and current information is SCD 2+. it is important to understand the past. The data warehouse is merely a tool. and the fact table data is retrieved. To successfully use your data warehouse over time. Using SQL may not be helpful in a situation where the goal is to overlook historical changes which have been made to a table. The success of SCD 2 is dependent on abilities of the tool. A company who knows how to use their data warehouse will be able to compete over time. present. It will not be useful to you and your company if you don't know how to use it. the reporting results may not be correct. If you want to use SCD 2+. The fact tables are connected to a single row. It may also be possible for other tools to use alternative techniques. and they can find ways to avoid repeating the same mistake in the future. you will still be able gather the fact table rows which are connected to the production key. a company must be able to study the data of the present to make smart investment decisions which will allow them to profit tomorrow. Many of them have done this. If the current dimension is constrained. the other will be used for historical data. A database management system can be utilized to identify either the historical or current columns.

or you can create different security systems for different types of data. and changes over a given period of time in order to make important decisions that can allow their company to succeed. There are two ways that you can set up security for your warehouse. A company that uses a data warehouse will need to learn how to analyze information. or they find it too complicated to use. Once they've done this. It is likely that your competitors are using data warehouses as well. Another issue that companies will want to look at is security. time plays an extremely important role. they won't use it. Data warehouses are directly connected to time. They can measure marketing strategies and other important information. Employees should be given all the necessary tools and training to allow them to use the data warehouse. the company has wasted money on a tool that their workers don't want to use. You can create a security system that covers all the data. While some mistakes may be made. For instance. the data warehouse will only give your company an edge if you know how to use it. you will want to place a higher level of security on your financial information than on marketing plans. If they don't like it. It is simply a place where you store information. connections. and it should never stop. However. as well as expensive. they should not be repeated. While data warehouses are important tools to have in the information age. The latter is generally the best. This is process that is ongoing.
The Difference Between Data Mart and Data Warehouse
The biggest decision facing most IT managers today is whether or not they should construct the data mart before the data warehouse. they will need to make decisions based on the information they've studied. In reality. It is also important for a company to decide how the data warehouse will be designed. Many organizations are under the false impression that data warehouses will automatically allow them to gain an edge on their competitors. A data warehouse alone isn't enough. There are multiple design approaches. they are not the cure to all the problems a company or organization will face.
. because it is not always good to have too much security on certain types of data. A company will study patterns. A data warehouse can become a powerful weapon when it is combined with tools such as data mining. When it comes to strategic thinking. and you will want to choose one that is useful or your organization. Companies should use data warehouses to figure out the best ways to market their products. The company that uses their warehouse with the most efficiency will be able to dominate in the market place. A data warehouse is a tool that can allow companies to measure their success and failures. the option that you choose should be dependent on your needs. It is also important for a company to maneuver quickly to find gaps in the armor of their competitors. If this happens. Many vendors will tell you that data warehouses are hard to build.Companies who use data warehouses will also want to place an empasis on learning.

the advertising department will have its own data mart. In addition to this. The data mart that they use will be specific to them. they are now trying to say that data warehouses are merely a collection of data marts. and will cause confusion among customers. Unfortunately. Some vendors will even tell you that you can create a data warehouse by simply building a few data marts and allowing them to grow.If you listen to some vendors. Again. If you only use data marts. Instead of being owned by one department. and other components that make up their data mart. and any data mart vendor that tells you this are looking out for their own best interests. many data mart companies are not willing to admit they made mistakes. A data mart is a group of subjects that are organized in a way that allows them to assist departments in making specific decisions. hardware. and this will demonstrate inconsistency. this is not correct. Another thing that separates data warehouses from data marts is that data warehouses
. Instead. As they continued constructing data marts. However. People who use data marts will have a hard time managing the interface between them. A data warehouse has a structure which is separate from a data mart. especially those that went to trade shows. While the data contained in data warehouses are granular. the information between data marts will become redundant. many people were confused by this. trying to use data marts in place of a data warehouse will not give your the results you are looking for. There is not way you can purchase a collection of data marts and grow them into data warehouses. a data warehouse will generally be owned by the entire company. a data warehouse is designed around the organization as a whole. data mart companies tried to tout their products as being the same product. It is natural that they would tell you about all the disadvantages you will encounter when trying to build a data warehouse. Despite these problems. Many of these customers purchased data marts and begin constructing them without data warehouses. Because of this. there are a number of problems you will run into by using this method. it is difficult to coordinate the data across multiple deparments. The information that is presented from each data mart will be different. each department will have full ownership of the software. For example. they begin to realize that the architectural structure was flawed. The problem with many data mart vendors is that they see data warehouses as barriers which stop them from earning a profit. even though they may look similar. In contrast. this view is inaccurate. the information contained in data marts are not very granular at all. you may be left thinking that building data warehouses is a waste of time. When data warehouses were first advertised. while the finance department will have a data mart that is separate from it. There are some notable differences between the two. Because of this. However. Each department will have its own view of how a data mart should look. There are a number of reasons why you will want to build data warehouse. It is also important to realize that data warehouses and data marts are not the same thing.

there are many differences between data marts and data warehouses. technical success. Much of the information that is held in data warehouses is historical in nature.
How To Properly Manage a Data Warehouse
When you manage your data warehouse. As the name implies. measuring a process is distinct from measuring a product. and it will make your operation run smoothly. you will need to study the way you measure quality. It is important to make sure you're not confused. it will demonstrate the information that is analyzed and processed by the organization. To do this. and these are economic success. As you can see. it will be high in quality. A data warehouse is not a panacea. When you maintain your data warehouse. It is also important for you to measure these activities over a given period of time. Your company must be able to deal with the many problems it will run into.contain larger amounts of information. The purpose of having a data warehouse is to allow you and your company to analyze the data you've collected and make decisions which are based on it. Measuring your data warehouse will allow you to determine if you are improving as a company or organization. Many people purchase data marts thinking that they are data warehouses. you must take the time to understand any mistakes that have been made. you will want to do more than simply place an emphasis on maintaining the data. You will want to maintain the data warehouse in a way which is directly related to caring for your customers. The information that is held by data marts are often summarized. Data warehouses will not contain information that is biased on the part of the department. there are specific areas of a data warehouse that need to be measured. If you don't take the time to make measurments. To successfully maintain your data warehouse. economic success is a method you will use to measure the financial impact that the data warehouse is having on
. your company won't be as competitive as it could be. However. There are a number of different ways you can measure your data warehouse. it is important to understand where the true value lies. It will evolve as you continue to use it over time. When it comes to data warehouses. However. and they are designed to process this information. It is also important to place an emphasis on planning. You will want to spend time measuring the activities that you are carrying out. and it is not the solution to all of your problems. When you measure your warehouse properly. It will cost your company less to develop a measurement program than you will have to pay if you measure data improperly. but this is not correct. One example of this is processes. To successfully manage your data warehouse. Instead. there are three common methods that are used. It is a database that contains large amounts of information. your information and views will be subjective. and political success. If the data is not organized in a way that makes it easy to analyze. it is important to place an emphasis on measurements.

it is important for you to make sure you are using the information that is held in your data warehouse. Technical success is the method of measurement that is the easiest to perform. Because of this. The data warehouse that you use should support your business plans. Many of them are not placing an emphasis on the management of meta data. this tactic is beginning to show a number of notable weaknesses. You should be practical with the technology you use. One tool that can allow data warehouse managers to deal with meta data is called a repository. the meta data can be coordinated among different
. One of the main problems with contemporary data warehouse management strategies is that information changes rapidly. Your data warehouse will have an impact on the quality of your business. it is of little consequence if it cannot be used. It will allow you to find out if your company is gaining or losing profits. They may use them to analyze specific areas such as customer service or financial issues. Both data and meta data are used with numerous data warehouses. For example. It should be able to effectively help you develop tactics that can be used to increase your profits. Political success is a measure of how much people like the data warehouse. it is not likely they will use it. What is the quality of the information that is provided by your data warehouse? No matter how much information you have. and you should be able to apply it to a given situation. they should look at their data warehouses to determine if they are on such a path. Many companies will create multiple goals that they will work towards by using data warehouses. and many of the technicians who manage these warehouses are trying to figure how to process and organize the meta data. The quality of the data is not important unless it can help you improve the quality of your business. Every company that wants to succeed should have goals.
How To Manage Meta Data Within a Data Warehouse
Many large companies are now implementing data warehouses. In order to make sure that these goals are being accomplished.your company. While this approach has worked well for some companies. Despite this. If they never use it. Because of this. this is a sign that your data warehouse is not politically successful. By using a repository. what will a data warehouse manager do when the developer of an application decides to change definitions within the application? The impact that can be created by this and other issues has caused many organizations to rethink their data warehouse management strategies. it is difficult to be consistent when managing data warehouses. If the workers to do not know how to utilize the information that is placed in the data warehouse. it is important to make sure you don't use too much technology. The information you store will play a role in the political success of the data warehouse. Being able to process meta data across a wide variety of different warehouses will make things easier.

A number of different departments would be able to share this information. it will be easier for your to customize information. a company can produce a data warehouse that is much more powerful and efficient. Using an industry standards tends to be better than a DBMS that was created by a vendor. If a new definition is created for a data mart implementation. It will also be helpful if the vendor works with the Microsoft Open Information Model. and it can assist you in building a powerful data warehouse. or modifying relationship characteristics. it is important to make sure it is based on an entity/relationship structure. In addition to this. By using these tools and strategies. or Oracle. If you decide to use a repository. a repository can support the change. Another thing that you can combine with a repository is API or application programming interface. it can also help you in the reporting process.
Federated Data Warehouse Architecture
. It will create a standard that can be understood among a number of different departments. it is important to make sure the database and information is documented. you can place an emphasis on the repository. There are a number of meta data extensions that should be supported by your repository. In order for this to occur.
When an API is used. the transfer of information will become easier. the repository could also handle a wide variety of different tools. One of the best advantages of using a repository is the consistency that will exist within the system. You will have a number of advanced tools that can assist your in managing your database. It is important for the repository to function well over the lifecycle of the data warehouse. In a nutshell. There are a number of things you will want to add to the repository to make it operate smoothly. modifying an entity type. One of the things you will want to add is a database management system. The meta data will be separated from other tools. In addition to this. A legacy model can help you in this area. and this will make it easier to modify. Some of these are adding an entity type. This information could include programs from companies like Microsoft. Even if the data changes.warehouses. When you use an industry standard database management system. It can help you in the development phase. and it can also help lower the cost of maintenance. there is no need to change the tools. IBM. it will be easier for a company to custom build a meta data management system. all the members of the organization would be able to share data structures and data definitions. A repository can help data warehouse managers in a number of different ways. The repository could act as a platform that would be capable of handling information from a number of different sources. By doing this. When you use an industry standard.

they will often wander if it is the same as a bottom-up dimenison approach. and each will have its own advantages and disadvantages. Once this has been done. Despite many of the disadvantages that are associated with this system. analytical applications. Many experts will tell you that there are a number of advantages to using a centralized data warehouse system. Each element will need to be rated based on the quality and accessibility. Once the information is analyzed. This architecture was designed to be an orthodoxed solution. it is one of the best for those who want a powerful data warehouse. and the other system will be able to use this information. The federated DW architecture has a data sharing system which is not as clean as the bottom-up approach. The ETL tool will host a meta data repository. and operational data stores.Federated data warehouse architecture is a system that works with numerous data mart systems. you will need to gather the candidates from the third step and study them to determine how important they are. A data warehouse is an important tool that can allow a company to profit. the federated DW architecture will have unique components that will hold feeds for numerous types of data. you will next need to document your current data warehouse system based on the data flow. and it can be reviewed and analyzed. The last thing you will need to do is create an iteration which is connected to the federated DW architecture. it is best to choose the candidate that has an excellent balance between risk and impact. A federated DW architecture is a system that is composed of multiple architectures. An example of this would be adding financial information to advertising data. After you have done this. you will need to figure out which data is useful for numerous systems. The federated data warehouse architecture will work with an ETL tool. an organization or company can make important business decisions that can allow them to compete. When many people first hear about a federated DW architecture. While you don't want too much risk. and it may also come from transformations or meta data repositories. it is also important to make sure you don't have too large of an impact. There are a number of ways that a federated DW architecture can be built. There are a number of build assessments that are available online. This will allow your company to better market its products to customers. Though the transfer of common data is the same. In a federated DW architecture. It will need to be reserved for the best candidate. These feeds will not be shared by other components. A federated data warehouse architecture will share information among a number of different systems. The first thing you can do is document the data warehouse system that you're already using with an enterprise data warehouse architecture. The information can be stored in a central location. Now that you've done this. At the zenith of this system is a diagram that will show you the numerous systems and meta data that is exchanged between them. Generally. Data
. the shared information may be taken from the mid-section of the system instead of the source. The data flow will come from multiple sources. There are a large number of architectures that can be used with a data warehouse. Critical master files will be shared.

Historical Information About Data Warehouses
Data warehouses have become an important part of information technology since the 1990s. and it is hard to argue with this assumption. it means little if it cannot be stored and analyzed in a useful way. transactions. Whether you are the owner of a small. However. and the data warehouses can also be used to find trends and relationships between various entities. and profits. are difficult to answer if you do not utilize computerized tools. At the most basic level. Many of these questions. and it could be said that the existence of data warehouses are a result of the Information Age. It has been said that information is the ultimate weapon. While this may work for some small businesses. A company can also learn more about their customers. the advent of the Information Age has changed this. However. data warehouses can be used to process multiple domains. If the information can be organized. entities that process millions of transactions within the course of a single day. Data warehouses are designed to provide information about the company as a whole. They are used to store information that a company can use for making important decisions. The data warehouse is proficient when it comes to dealing with database queries. In addition to this. there can be no doubt that you have questions about your company that you would like answered. it can be studied. A company can study this items to find patterns which can allow them to improve the quality of their business. it became crucially important for large corporations to process data and use it for the purpose of making strategic business decisions. Because the competition between these companies has become so fierce. They can pull information from various databases. and this can be done in very unique ways. processing this information and using it in strategic ways has become the fundamental challenge. medium. especially those that deal with trends and business processes. and this can allow a company to develop a number of strategies that can be used to solve problems. the company can learn how to avoid costly mistakes. or large business. it has become
. A data warehouse is an indispensable tool for large companies and organizations. it is futile for multinational corporations. because a large amount of space is necessary to store the data that must be reviewed. Information can provide the knowledge that companies need. While information can be useful. As the global competition between corporations continued to grow in the 1980s. data warehouses are somewhat similar to databases. getting a hold of useful information was the biggest challenge to companies and organizations. It is no longer simply enough to store information in a database.warehouses are extremely advanced. while databases are typically used for one domain. They can provide executives with crucial information that will allow them to compete on a global scale. Throughout most of history. While information has become much more easy to acquire.

The first thing that a company will want to have is a consensus within organization. Like all forms of new technology. and they can deal with specific subjects. However. To do this successfully. Another problem that plagued the industry during this time was the cost. a market has been created for small to medium sized businesses. While databases are used primarily to store information. there are a number of important principles that companies will want to follow. transactions. this allows the corporation to make strategic decisions that can enhance their profitability. In most cases. this price range is now available today. many large companies learned the hard ways of implementing their data warehouses.
. A failure to create a consensus within the organization is one of the key reason why many data warehouse projects fail. During the 1990s. By finding various relationships between their customers. and business processes. there are a number of reasons why you may want to consider using a data warehouse.imperative for these companies to spend a great deal of time analyzing their data. trends. they can cause problems for a company if they are not implemented properly. The organization should be guided through the process of setting up the data warehouse. data warehouses where expensive when they were first introduced. and it is to these companies that vendors tailored their products. and they are designed to answer specific questions. Simply put. and the costs involving with implementing a data warehouse have dropped substantially. and the employees and managers should be able to understand the purpose in using it. data warehouses will analyze information. it can become problematic for companies that fail to use core principles. One of the most powerful benefits of data warehouses if the ability of company managers to rapidly pull information from them within a short period of time. data warehouses must be properly maintained and implemented. and they will change the face of global competition for many
Data Warehouse Business Principles
While data warehousing is a promising technology. data warehouses can be seen as a type of digital business advisor. While data warehouses are very powerful if they are utilized properly. a failure to properly implement a data warehouse can cause a company to lose tens of millions of dollars. In addition to having a proper design. data warehouses will be read only. Only Fortune 500 or 1000 companies could afford them. since many large companies have now implemented a data warehouse. While they are still quite costly today. Whether you are running a small or large business.000 during the 1990s. In this light. While it was not possible to implement a data warehouse for $100. Many of these problems will be financial in nature. there were not in the price range of most small to medium sized businesses during the 1990s. They have played a fundamental role in the advancement of technology.

The next business principle you will want to focus on is data integrity. Many companies will create a methodology for data integrity. the costs involved with setting of the data warehouse will be much lower. It should be constructed in a way that reduces the chances of the data being duplicated or inconsistent. Any business request that are made should easily be taken care of.
. another core data warehouse principle is operational efficiency. This front end should be based around security and the roles of the users. When this is done properly. and this is fine so long as it is done with the end result. Everything should be done from a practical standpoint. Another principle that companies will want to pay attention to is the efficiency of the implementation. because if the implementation is done correctly in the early stages. and workers and managers do not see the need of using it in place of their traditional systems. It could be argued that this is one of the most important aspects of data warehousing. Many vendors focus too much on the technicalities of the data warehouse. the operational efficiency of the data warehouse will be much more efficient over the long term. and of business intelligence in general. Another pivotal aspect of a successful data warehouse is the user friendliness. and errors shouldn't be of major concern. After user friendliness. At the very least. The design for the data warehouse should be simple to implement. When a data warehouse is designed. As the name suggests. it is generally best for it to be within the range of users of have the least technical skill. and the most important is scalability. Once the data warehouse has been implemented. it should be much easier to support. The implementation is one of the most fundamental aspects of creating a data warehouse. the data warehouse project will become tedious if this design is not easy to comprehend. and this will allow the company to stay within its budget while helping it achieve its needs. and the needs of the company can be met early in the process. it is important for a company to focus on simplicity. Countless data warehouse projects fail because the data warehouse is not use friendly. The operational efficiency of a data warehouse is closely related to its implementation. the system should have a minimum learning curve. Even if a company has a data warehouse that showcases an impressive design. The process of data integrity begins when the data warehouse is constructed. and fail to take the end user into consideration. The best way to make the data warehouse user friendly is to create a standard front end that is used throughout the company. The implementation of a data warehouse plays an important role in its operational efficiency. this is the efficiency of the data warehouse itself. There are certain IT principles that a company will also want to consider. If a company wants to succeed with their data warehousing project. a project like this can become costly. The data should also be highly integrated. Most importantly. While this will be difficult to achieve given the complexity of the data warehouse. the data must have a high level of integrity.

and while some of this information is external. In most cases. and the identifications of trends. forecasting. and the proper hardware and software must be implemented. While analysts will often be responsible for working with informational data. having business skills are useful as well. and the user must be able handle each of these areas efficiently. Data warehouses are specifically designed for business executives and anyone who has the responsibility of making strategic decisions. By using a data warehouse. The best way to deal with the scalability issue is to build it into the system from the very beginning. and they will also play a fundamental role in information processing. Some of the things which it is commonly used for include market research. This information will be taken from various sources. There are a number of reasons why a company should want to separate operation data bases from those that are information based. One of the most important reasons for this is because the users of both forms of data are different. This will alleviate any problems that may be caused by design inconsistencies. The
.Many companies run into problems when they try to add scalability to the design of their data warehouses. an integration of various support systems and programs that are knowledge based. The data warehouse is a tool that is designed for the long term. It is more of a strategy or a process. they will merely act as a repository for data. and presents it in a way that allows users to use it for a number of different purposes. Despite this. The data within the data warehouse will be broken down into subject areas. While operational databases played an important role in the past.
Understanding The Data Warehouse
To understand the data warehouse. the users are expected to have a great deal of technical skill. it is important for you to realize that it is not a single object. other parts of it are internal. This scalable structure will play an important role in the foundation of the system. the information obtained from a data warehouse will be used for strategic analysis. they are not used directly for information processing within modern data warehouses. It is also important for the data warehouse to be scalable. To run a data warehouse efficiently. In addition to the data warehouse itself. and they must also be able to set priorities for the information that is stored in the data warehouse. It takes the data from the organization as a whole. In addition to this. In most cases. To use a data warehouse. The goal of using a data warehouse is to allow businesses and organizations to make strategic decisions. many people make the mistake of believing that a data warehouse is merely a tool that is used for collecting data and making reports on it. a company will have access to information that is detailed and consolidated. The data must be cleaned and transformed in a way that allows it to remain accurate. the maintenance of the data is equally important. administrative employees will spend more time working with operational data. the user must be able to correctly identify the business information that is comprised in it.

Another issue with many data warehouses is user friendliness. data warehouse projects have failed because the tool was not user friendly. A help desk will generally be of great use. and the goal of modeling it is generally to speed of reporting. If the employees have a had time utilizing the capabilities of the data warehouse. many companies seek to alleviate the problem by implementing a data warehouse system. It should also be emphasized that running reports on a server via transaction systems can be quite challenging. Summarization also plays an important role in the function of the data warehouse.
Because of the complexities surrounding the data warehouse. Before the introduction of data warehouses. The vast majority of companies wish to set up transaction systems so there is a good chance that these transactions will be completed within a desirable time frame. the user must be educated in how to efficiently use it.
The Benefits of Data Warehouses
There are a number of reasons why many large corporations have spent large amounts of money implementing data warehouses. The success of a data warehouse is not just dependent on the tool itself. and this is not a necessity for operational databases. The reason for this is
. this could limit the success of the company.consistency of data within the data warehouse is extremely important. The most fundamental benefit of using data warehouses is that they store and present information in such a way that it allows business executives to make important decisions. Instead of looking at an organization in terms of the departments that it comprises. Another benefit of data warehouses is their ability to handle server tasks connected to querying which is not used by most transaction systems. reporting. Because of these challenges. but it is also dependent on the implementation and how the company educates the employees in using it. most companies used various databases to store information that was related to transactions. and millions of dollars were lost. This will often be done via a star schema. and it is generally not recommended for transaction processing systems. Another powerful benefit of data warehouses is that they allow compnies to use data models for querying tasks that are quite difficult for transaction processing. data warehouses allow business executives to look at the company as a whole. and it is important for the user to make sure the correct level is created. The biggest problem with reports and queries is that these entities can reduce the chances of a transaction being made within a good time frame. In addition to this. and designing tutorial for the user can be helpful as well. or other business processing. There are a number of ways that data can be modeled. the technology for both database types are inherently different. The information data is more closely related to historical trends. In some cases. thus allowing the organization to make important business decisions.

and this is something that companies will want to pay attention to early on. While there are a number of challenges to these scenarios. a repository for transaction processing systems that have been cleaned. the older data and the recent data may be stored in the data warehouse in a way that gives the user control over the response time. and it can also be done from outside sources. it is important for companies to realize that data warehouses are not a panacea. the server units may speed up the transaction process. The can give the company a forecast on how the company is performing as a whole. a solution to all the problems a company will face. they can bring a large number of advantages to the companies that use them. While there is a degree of truth to the statements that are made by many vendors.
. workers who do not have a lot of technical skill will often run into problems when trying to perform certain tasks. Despite this.
The Disadvantages of a Data Warehouse
Many vendors will spend a great deal of time talking about the advantages of data warehouses. but they will slow down the querying process. data that was considered to be old would often be removed from transaction processing systems. In older systems. The data can be reported against them. When data warehouses are implemented and designed properly. Before the advent of data warehouses. In most cases. For tasks that required querying. Perhaps one of the most important benefits of data warehouses is that they set the stage for an environment where a small amount of technical knowledge about databases can be used to write queries and speed of the maintenance of these queries. companies that had large amounts of data may have had problems if they wanted to sort through it frequently. Data warehouses are unique in the fact that they can act as a repository. companies that wanted reports from numerous systems had to produce data extracts and run special logic programs to combine this data. and why companies need them if they wish to survive in the global market. a company can handle them if they take the time to establish the right procedures.because certain modeling methods can slow down transaction processing systems. Simplicity plays an important role in the success of a data warehouse. and it may not require the transaction process systems to be fixed of calibrated. Workers may run into some challenges depending on the information they need. Data warehouses can be highly efficient because they will allow the user to make queries of data on a regular basis. At the same time. This can be done from numerous transaction systems. and it can allow the executives and managers to make crucial decisions that can help a company succeed. and this includes those which are both positive and negative. Being able to maximize the efficiency of a data warehouse requires the company to look at it from multiple views. This was done for the purpose of making the response time easier to maintain. Most data warehouses can be set up in such a way that simple queries can be written by workers who do not have a lot of technical skill. Even then. this strategy worked fine.

many companies aren't patient enough to wait for the implementation of a data warehouse. Depending on the conditions. While vendors in recent years have begun tailoring their products towards small to medium sized businesses. Even today. it could be said that data warehouses can take on a life of their own. Some experts have even said that these complications can eventually strangle the business. most data warehouses are outside the price range of companies that don't fall under the Fortune 500 or 1000 category. Because of the speed of the business world. In the past. only the truly wealthy companies could afford them.The ultimate goal of a data warehouse system is to store historical information about a company's transactions. When you combine this with the fact that the costs involved with maintaining the data warehouse can become larger. and much of this data is made available in basic reports. One of the criticisms which are commonly made of data warehouses is their complexity. This is especially true for many small to medium sized businesses that are analyze their transactions with needing expensive programs. the end user will not have a strong interest in older processing data. it is easy to see why companies should be careful in their decision to use it. Like all advanced technology. The second problem that has become quite common with data warehouses is their cost. and they want them fast. and its value may even be limited. It is also important for companies to pay attention to the data aspect of the warehouse. In one case. when data warehouses were first introduced. The implementation of a data warehouse can be so complex that it can make the business processes harder to deal with. it took a company 18 months to fully implement the system. However. and it will take time before a company begins seeing a return on their investment. and present this informatin in a way that will allow business executives to make important decisions. The ROI for data warehouse projects will typically be much lower than vendors promise. Many of the markets that businesses operate in today are in constant transition. Data warehouses are a technology that has brought a great deal of success to many
. If a company is not capable of placing a lower emphasis on some processes. data warehouses may be too much for most businesses. In some situations. Many firms simply don't have the patience to wait for these returns. this data may make up only a small part of the information that a company needs to operate. the data warehouse can cause the business environment to become much more cluttered. Most firms today want results. It must be emphasized that placing data in a data warehouse for the sake of adding for no specific purpose can reduce its value. many of these companies may not see the need of using a sysem that is overly complex. it wasn't uncommon for a data warehouse project to take many months for implementation. and could inevitably become a failure. Because of the complexity of these systems. They don't see the need for waiting months on a system that is unproven. In other words. it may not be necessary to use a historical system.

Despite this. To understand the data. many vendors paint a rosy picture and fail to talk about the challenges that a company will face. It has been said that knowledge is power. The second rule of data warehouses is to understand the data that is stored. it is important for companies to analyze their data carefully before making decisions that are based on it. It is unwise to just accept data as it is. The implementation of a data warehouse will often require the user to make some modifications to the schema of the database. One of the most common problems that can occur in a data warehouse is when the same pieces of data appear in various parts of the system with different names. but the name of the department may be placed in the system twice under different names.companies. Companies will want to perform an analysis each day of the databases that are connected to the data warehouse. and the other name could be an abbreviation. Knowledge that is stored and unused is potential power. The very first rule of thumb is to realize that data warehouses are challenging to use. Once these relationships are found. For instance. their are some general principles that you will want to pay attention to. it is important for them to establish rules and regulations with which to use it. this error percentage should not be allowed in the data warehouse. Many experts say that at least 30% of the info they give out may not be consistent. When you consider the fact that many large scale data warehouses can cost millions of dollars to purchase and implement. Another important rule of thumb is to learn how to find entities that are equivalent or equal to each other. two departments within the organization may be helping one customer. One name could be spelled out. but it will also allow the organization to use it much more efficiently. Only after they've done this analysis can they decide if a data
Rules to Use With Your Data Warehouse
Once a company has successfully implemented their data warehouse. This is done because of the fact that the vendor is interested in selling the product. This can
. These principles will not only make using your data warehouse easier. without carefully looking for errors or other problems. and error percentage of 30% is not acceptable. Companies must analyze the organization carefully to decide if a data warehouse is conducive to their needs. the analysts must be able to find relationships among numerous systems. While different companies will have different rules when it comes to handling their data warehouses. If the user does not understand the various relationships among the systems. but this is only a half truth. However. To solve this problem. they must be maintained when the data is moved within the data warehouse. they may be prone to generating errors that could compromise the accuracy and efficiency of the system. One of the most problematic things about this is the company may not notice the error if they are dealing with an operational unit that is transaction based.

While there is nothing wrong with this. Metadata can be defined as "the data about data. cleans. it is best to find out what the users of the data warehouse need next rather than what they want right now. While the percentage may or may not be as high as 80%. A failure to prepare for these issues is one of the key reasons why many data warehouse projects are unsuccessful. Look for vendors who are able to integrate metadata from numerous sources that are disparate. This will make mergers much easier when they occur. To deal with this issue. Some experts have said that the typical data warehouse project will require companies to spend 80% of their time doing this. The data transformation product is crucially important. companies will want to establish a standard database structure. Not matter how well a company prepares for the project management. While most projects will begin with specific requirements. Perhaps one of the most important principles of data warehousing is to use metadata in a way that supports the quality of the data within the data warehouse. serious problems can arise when each them decides to store information in a different way." It is the data which describes the data within the data warehouse. One of the biggest challenge that companies will face is trying to harmonize the metadata across multiple vendor tools.create serious problems in the system if it is not corrected. companies will want to make sure they generate the metadata and use it for interfaces or other products. and companies must choose the product carefully. Once the end users see what they can do with the data warehouse once its completed. and the best way to solve this problem is to use a data transformation tool. It will also record a history of this process. Another issue that companies will have to face is having problems with their systems placing information in the data warehouse. one thing that you must realize is most vendors will understate the amount of time you will have to spend doing it. One of the first issues companies need to confront is that they are going to spend a great deal of time loading and cleaning data. they must face the fact that the scope of the project will probably be longer then they estimate. and loads data into the data warehouse. It is also important for companies to make sure they choose the right data transformation products.
Data Warehouse Issues
There are certain issues surrounding data warehouses that companies need to be prepared for. they will conclude with data.
. Because many large companies and organizations are comprised of many different departments. One of the situations where this occurs frequently is during mergers. it is very likely that they will place higher demands on it. extracting it can be even more challenging. While cleaning the data can be complicated. To avoid this problem. A data transformation product is a device that extracts.

Many companies also make the mistake of not budgeting high enough for the resources that are connected to the feeder system structure. they may also be required to purchase certain forms of technology. The good news about this is that the mainframe utilities are often proficient in this area. When of the most common issues is when controls are not placed under the names of customers. In addition to this. During the cleaning process. It should also be noted that a company will often be responsible for storing data that has not be collected by the existing systems they have. Regardless. many companies placed an emphasis on defining the data warehouse as a system that was distinct from a standard operational system. Many of these fields will have information that is descriptive. especially if the company wants them to use the data warehouse frequently. they will find that problems that have been hidden for years will suddenly appear. you will be expected to do a great deal of sorting. Once this happens. This can be a headache for developers who run into the problem. the data will need to be validated. Some developers may refer to this as being a granular issue. Some users chosoe to construct aggregates within the mainframe since aggregation will also require a lot of sorting. The developer of the data warehouse may find themselves having to alter the transaction processing systems. When data is placed in a warehouse. One of the most critical problems a company may face is a transaction processing system that feeds info into the data warehouse with little detail.
. the business managers will have to make the decision of whether or not the problem can be fixed via the transaction processing system or a data warehouse that is read only. When data warehouses were first introduced in the 1990s.When a company enters this stage for the first time. and the only way to solve it is by storing data into the system. companies will want to construct a portion of the cleaning logic for the feeder system platform. It is important to make sure that the information that is placed in the data warehouse is rich in detail. This may occur frequently in a data warehouse that is tailored towards products or customers. It should also be noted that many end user will not use the training that they receive for using the data warehouse. it is important that the be taught the fundamentals of using it.
Crucial Requirements For Successful Data Warehouses
There are certain requirements that companies need to meet if they wish to use their data warehouses effectively. This is especially important if the platform happens to be a mainframe. To deal with this. Many companies will also find that some of their data is not being validated via the transaction processing programs. In a situation like this. there will be a number of inconsistencies that will occur within fields. This will cause headaches for the warehouse user that will want the data warehouse to carry out an ad hoc query for selecting the name of a specific customer. However. it is a problem you will want to avoid at all costs.

Once this is done. The data warehouse should be constructed in a way that allows it to evolve. It will be frustrating and tedious to have to change the schemas every time the company needs to adjust to a new change. The only thing that remains constant is change. While there were many successes. all the parts of the data warehouse should use the same structure. the company can add new information to their system without having to make tedious changes. The second requirement that companies will want to meet is the ability to deal with changes when they occur. a department must be able to design their data marts in a unique way. Once this is done. and they can focus on more important issues. However. Remember. this information should be made available to the rest of the company. In addition to this. To succeed in the current market. Technology has also advanced to the point where OLAP engines can focus on pulling out the data rather than placing it within the data warehouse. The fourth requirement that companies will need to have is the ability to easily drill to the most basic form of atomic data. By using rapid deployment. A company must be able to add additional information to their data warehouse without having to modify any of its components. it will be much easier for the company to construct the parts and index them. It should also be noted that the field of dimensional modeling has greatly improved over the last decade. This greatly increased the costs involved with building the system. The first thing companies will want to do is go from a centralized development strategy to one that is decentralized. To do this rapidly. and the data warehouse was also seen as being a centralized copy of data that is operational. Querying the parts would also become much easier. The third requirement that companies will want to meet is rapid implementation.
. One of the things that has improved the data warehouse industry is the increasing computer processing power. and it can be done much faster with a high level of efficiency. In the past. it took companies months and sometimes years to build a data warehouse that was centralized. Even though individual departments will need their own small warehouses to answer crucial questions. the data warehouse can be constructed in pieces. many companies have been to change their perspectives on how they see data warehouses.This view was shared by many companies. the development should also be incremental. the goal of a data warehouse is to give a view of the company as a whole. there were many more failures. and a company must prepare for this. Despite this. and the company wasted a great deal of time. The 1990s were a decade of trial and error. over the last decade. it is important for companies to create a framework which allows these departments to share their information with the rest of the company. Because this practice cannot be stopped. companies need to understand the requirements they must meet if they want their data warehouses to be successful. One thing that companies must realize is that it is inevitable that smaller departments will create their own small warehouses. This follows closely to building a system that is decentralized rather than centralized.

The information is placed in a single unit. and this led to a lack of efficiency. they will be able to make decisions with a great deal of confidence. Another requirement that a company must have are data marts that when combined can create the totality of the data warehouse. Every successful business gathers and records information that is related to their customers and various transactions. Data will be stored in the warehouse from multiple sources. This is still challenging today for companies that don't use data warehouses. One of the most powerful benefits of a data warehouse is the fact that operational forms of data can be optimized for a certain level of efficiency. and it is important for departments to access this information without having to give their employees a great deal of training. Data warehouses are useful because they can allow a company to give managers and executives crucial information that will allow them to make better decisions. its data about data. and this is
. data warehouses would be down for certain periods of time. and the company can get a clear picture of how their company is performing. The data is considered to be nonvolatile.The vast majority of data marts in the company will need to use atomic data. The process of cleaning and transforming data is known as ETL. In other words. Many of these businesses will use an OLTP. Transformation. Once the data is stored. In the past. Data warehouses are useful because they collect data and remodel it. or Extraction.
Why Data Warehouses Can Be Useful
A data warehouse is a tool that is constructed to give a specific view of data that an organization or company can gather during the course of carrying out various processes. and Loading. Metadata can be defined as the information on the data that is stored in the warehouse. In the past. it was very difficult for managers or executives to get information about their company as a whole. It is also important for companies to make sure they data warehouses are available 24 hours a day. Having the data warehouses online 24 hours a day allows the company to be highly efficient. the information they retrieve can be inconsistent. The data marts should be comprised of the fundamental atomic data. Data will be placed in the warehouse periodically. it must be cleaned and transformed. In a day and age when the decision of one executive can make or break a company. When a company uses a number of different systems. One concept that you will want to become familiar with is metadata. Properly caring for the data is an important part of maintaining a successful data warehouse. Metadata can be broken down into three categories. this is crucially important. and they follow set rules and procedures. and it will be done in batches. or online transaction processing tool. because it is inefficient to replicate the data measurements throughout the company. the data will be stored at times when the company isn't extremely busy. The data warehouse is specifically designed to give managers information about the company as a single entity. Most importantly. Most companies store data for the long term. In most cases.

They will take the information they are given. it can be truly called a data warehouse. Despite the fact that all data warehouses are comprised of these three elements. The three important themes that all data warehouses share are processing time. When a user studies the sales of a product at a specific level. The administrative is related to the columns and tables of the warehouse. and drilling up.operational. If a system has all of these factors. there are certain fundamental themes that they all share. and it runs with a high level of efficiency. such as from the manufacturer. you will want to add the appropriate row headers. The decision support programs will fall under one of three categories. The term "drilling down" is used to describe the addition of a row header. history. As the second name implies. In this article. This data is especially important to those who will be making the key decisions. the query will present them with information that is related to the sum.
Fundamental Themes For Your Data Warehouse
While each data warehouse may differ in their size. These small data warehouses are referred to as being data marts. SQL. Data mining will typically use logistic regression or specific algorithms. they all lay the foundation for structures that are truly powerful. These applications are designed in a way that will allow managers and executives to get important answers to their questions. and these are data mining. as well as the data marts. Because many managers in the company will have different needs for the data. the row header will be added to a "select" statement. The operational metadata deals with the errors. These answers can assist them in the decision making process. In most cases.
. Once this is done. drilling down. As the name suggests. The data mart will get its information from the central data warehouse that is being used by the company. that it allows the decision makers to look at summarized data before looking for information that is much more specific. These programs will get their information from the data warehouse. and sales. or complexity. Data mining is quite powerful because it allows an AI or neural network to sift through the dat looking for important trends or relationships. and they will use it for querying purposes. and it also deals with the rules by which the data is maintained. connections that are impossible for humans to find within a short time period. and usage. the query will also have information that is related to time or other units. particularly within a relational database. or OLAP. the data warehouse will show numerous rows which list the brands which are sold. If you want to drill down further to the brand that the manufacturers sale. business metadata deals with various business terms. scope. business. many of them will construct smaller data warehouses that are tailored towards certain subjects. The data can be presented in such a way. In addition to this. and administrative. manufacturer. I will describe the fundamental themes that make up all data warehouses. The last part of the data warehouse is the decision support program. and I will explain why they are so important. it deals with the operational issues surrounding the data.

It could basically be said that grouping columns and row headers are identical. There are a number of things a company should do if they wish to build a system for drilling down. but most of them can never explain how this happens. the atomic data must be comprised of the same schema.A row header will often be referred to as being a grouping column. they must first understand how it is built. The atomic data may be hidden. Many experts have said that distinct schema programming is the bane of many data warehouses. the global competition among companies has become more fierce. the brand unit and the manufacturer unit can generally be found in the same dimension table. If the information contained in the example above is located in a dimensional star schema. There are actual some people which support this structure. especially at the user interface level. but this doesn't mean that a company can't fall victim to it today. it is one of the leading causes of having an architecture that is a strange structure. The reason for this is because each schema will need to have an application that is custom built. and this will make it easier when the drill down procedure is used. It is also possible for the user to place the attributes of a brand within the query. They will first need to acquire and use query tools that are ad hoc. When a company fails to do this. this will make the query much more simple. and they can drill down in a certain manner. and it may only be accessed after a user has used the drilling through process.
What You Should Know About Building a Data Warehouse
As we move further into the Information Age. and many of them are relying more on data warehouses to help them make critical decisions. Before a company can use a data warehouse to achieve their own goals. and the reason for this is because the atomic data is much more dimensional than other forms of data. These are generally people who have never used query tool that was designed by a commercial company. Once a user runs a query at the level of the manufacturers. they can see a collection of characteristics for the dimensions of the products. If the attributes for the manufacturer and brand are contained within the same dimensional table. The atomic data should be easily accessible. It is best to use a large amount of atomic data during this stage. When two points are combined together. Once this is done. It is crucial for the data warehouse to support drilling down. then this reduces the number of adjustments that need to be made to the SQL. The reason for this is because all the items that are not connected to an operator such as SUM will need to be identified in the SGQ group with a certain clause. It should also be noted that the atomic data is more expressive as well. the user can run a query again.
. These should be tools that will showcase the drill down options without the need for distinct schema programming. This problem was quite prevalent during the 1990s. In other words.

While some areas of the Internet like e-commerce may be highly accurate. Once the data has been cleaned. it can be analyzed properly. Dealing with personal names can be very challenging because many people may go by numerous names. and whether or not it comes from a program that is operational. Many of the users may not have a technical knowledge of the warehouse. If a person lives in an apartment. The quality of the data within the warehouse is very important. and make preparations for them. When data is pulled from the Internet. it is also important to make sure that data is defined. a company will need to analyze the rules of this data before they allow it to be placed within a program that is operational. and this is why it is so crucial for the analysts and developers to make sure the data is defined. Another issue that companies will want to consider is how updated the data warehouse should be. Queries which are highly intelligent will make the information that is found that much more valuable. This schema should only be limited by the database management system itself. Every company that decides to use a data warehouse must figure out how they will store the data within it. other parts of the Internet may be highly inaccurate. one important aspect of running a successful data warehouse is making sure the data has been cleaned. Analysts will want to look at the potential queries that will commonly be made by the user. It should also be noted that it is critically important for companies to understand the rewards that surround placing data in the warehouse properly. Some of these may be legal names or nicknames. If the data does come from an operational program. they can enter the information in a number of different ways. The warehouse schema should be set up in such a way that it allows the largest number of questions to be asked and answered. and can play an important role in the business decisions that are made by the company. Because of this. In addition to placing data within the data warehouse. The same problem may also occur when dealing with addresses. this is of great importance. and it is important for companies to make sure a definition of the data is made.
. Some of the things that the company will want to look at is the source of the data. The maintainence of data plays an important role in the construction of the data warehouse. The recent advent of software that can automate the data cleaning process and combine this process with the transport of data via an operational system is useful. The number of possible queries for a data warheouse are quite numerous. It must be accurate if the company wants to make good decisions based on it.Some of the greatest challenges involving a data warehouse will be seen when it is implemented by the company for the first time. To solve these problems. it is important for companies to construct programs that are capable of making correlatoins between data that is similar. A company must understand that there is a big difference between moving the data into the warehouse versus changing the operational systems in a way that makes them more friendly to the data warehouse. and this can cause inconsistencies in the system.

The ultimate goal of a company should be to design a schema that is close to real time. which stands for extract. When a database is designed. A company can't afford to make decisions on data that is old or obsolete. When data is brought into the data warehouse. It should also be noted that various decisions will be made based on differing views on the information that is stored within the data warehouse. A partial index key is powerful because it will allow you to either combine or split the data throughout the database. since this will speed up the ETL process. and the customer may no longer live at that address. it is crucial for it to be properly maintained. as well as partitioning.
How To Rate Your Data Warehouse
Data warehouses have greatly evolved over the last 10 years. and processing extensive amounts of data will generally need to be done via VLDBs. When the data warehouse is being updated in near real-time. In my opinion. The process of transforming data can be very costly. or very large databases. They now have their own transaction systems as well as their own design structures.The maintenance of the warehouse should be done based on the volatility of the data within it. this will allow the company to make decisions which are highly accurate. Despite this. and it is something that a company will not want to take lightly. and it can be partitioned to the data that has been split. Another important part of the data warehouse that you will want to pay attention to is the structure of the data. it is important for companies to take the time to rate their data warehouses. it may be possible to partition certain elements within the data warehouse by breaking data into dual columns. The input data should be consistent with the decisions and the goals of the organization as a whole. One way to rate your data warehouse is to determine if you've laid down strict rules for the maintainence of data. When you analyze the various input systems. the company will have a good idea of the efficiency of their systems. transformation. In some cases. Structuring the data properly will be crucial for the total performance of the data warehouse. Each column will act as a key. an address that was entered for a customer 18 months ago may not be current. The dimensional design has become the most prominent method of construction for data warehouses in the 21st century. it is important for you to come up with criteria to determine how much transformation each piece will need. because external pieces of information can be just as valuable as the information that is gained within the company. By doing this. One of the first things a company will want to pay attention to is the ETL. It is important for a company to make sure the data warehouse is comprised of parallelism. it is important for a company to place an emphasis on external data. and load. Most companies place an emphasis on the data they obtain within the company rather than data that is obtained from external sources. this can be a costly mistake. A partial key can be created by simply taking one column and
. Data consistency is one of the most important factors in the success or failure of a data warehouse. For instance.

This is a very effective method when you want to randomize information throughout the database. The most basic structure for information is a table. By using the Cood rules. The second column can be used for the purpose of data partitioning. it could be said that a data warehouse can be rated based on the procedures that a company uses when dealing with it. Learning how to rate your data warehouse is very important. The OLTP uses the field of data modeling to utilize the Codd laws of normalizing data in order to create a high level of integrity with the data.
How Data Is Stored Within a Data Warehouse
The data that is stored in the data warehouse is just as important as the data warehouse itself. The data within the warehouse must be properly maintained. Having a fundamental understanding of how this data is stored can be useful in the successful implementation and utilization of a data warehouse. Being able to effectively implement a data warehouse is not enough. The ability for a company to rate its database is critically important. they may need to assemble data that comes from hundreds of millions of transactions. One term that you will want to become familiar with is OLTP. and it may be possible to spread the data across dozens of segments within the database. the program will be able to give the company the results it needs.breaking it into two columns. Having said that. It is important for companies to realize that they will make crucial decisions based on the data that is contained within their warehouse. As you can imagine. It should also be noted that it is possible to create keys from an algorithm. OLTP databases are useful because they deal with the information that is specifically related to a single transaction. The reason for this is because only a small amount of the data is effected when a transaction is made. Managers who specialize in relational databases are particularly skilled in handling the relationships within the tables. and this will inevitably lead to a loss in profits. However. The biggest challenge that companies will face is when they need to assemble the various bits of data into a record that can be used for analysis. the data from a single transaction will often be stored in a large number of tables. When the database uses a design that is highly normalized. it could easily lead to disaster. When a company is ready to analyze its data. The numbers could be evenly distributed in numerous places within the database. this must be done carefully so that the end user access is not harmed. this can lead to situations where poor decisions are made based on the information that is presented. or it hasn't been cared for properly. If the data is not managed properly. elaborate information can be split into a structure which is simple and basic. If the data is inconsistent. This could hurt the productivity of the company. this is a tremendous workload. but it is generally best to keep it
. and this allows the data warehouses to perform at a very high level of efficiency. or online transaction processing systems. If enough time is allowed. The Codd laws define 5 important guidelines that must be used if data is to be stored with a 3rd normalization level.

it will be done within a single domain. When this is done. and these are the normalized approach and the dimensional approach. Even if the data warehouse has a high level of efficiency. One of the most impressive aspects of OLTP databases is they are designed to provide a high level of performance by handling the applications in certain ways. Once this has been done. If the data is analyzed. To understand how data is stored within a warehouse. With the dimensional approach. the data within the data warehouse will be held in a third normal form. and these areas will define the data. Each system will place an emphasis on one subject. The purpose of using a data warehouse is to bring in data from a number of different databases with the purpose of analyzing it. It is also important to realize that the data warehouse must be capable of handling large amounts of data that is collected over a period of time. it will be useless if the wokers are not trained in how to use it. and they will define certain values.
How Does a Data Warehouse Differ From a Database
There are a number of fundamental differences which separate a data warehouse from a database. Because advanced queries will be used. It is best to store the data in its most basic form. All these approaches can be broken down into two categories. The facts will be number based. It is also important for the data to be structured and reformatted on a regular basis.
With the normalized approach.disconnected from the database that is OLTP based. and it will not deal with other areas. The programmers who design these applications will often be adept in understanding the limitations of the technology. data warehouses deal with multiple domains simultaneously. but multiple domains are not uncommon. There are a wide variety of methods that can be used for the implementation of a data warehouse. it should be noted that there may be times where an emphasis is placed in different requirements. One of the most powerful advantages of this approach is that it is relatively simple to add new data within the database. In contrast. Some of the separate units that may be comprised within a database include payroll or inventory. The biggest difference between the two is that most databases place an emphasis on a single application. This analysis will be used for reporting and management. the program can be easily used by uses who are novices. The dimension will hold reference information. One reason for this is because the reduced performance of the system when the two are connected. and this application will generally be one that is based on transactions. However. you must understand the purpose of using the data warehouse. the tables will be collected together via subject areas. the data will be broken down into facts or dimensions.
. the data warehouse must support formats from various systems. because this provides a high level of flexibility in the reporting process.

Many company managers are interested in transactions that occured over a certain time period. Dimensionality is a concept which shows that the data is connected in various ways. the data warehouse finds connections between them. and structure of these two data types are quite different. Generally. it will not be found in a decision support data warehouse. the operational data will be placed in a relational database. However. This allows the data warehouse to show how the company is performing as a whole. the decision support data must be broken down into different parts of aggregation. The timespan deals with transactions that are atomic. Granularity is the third concept that separates operational data from decision support data. the data must be placed in a certain number of tables. The data that is stored in a data warehouse will often be multidimensional. If a sale has just been made. While this system may be highly efficient in an operational database. If you wish to take out a single invoice.
. and it offers support for things that are not readily used by operational data. operational data will deal with a short time frame. format. as well as some medium sized businesses. Another powerful aspect of data warehouses is their ability to support the analysis of trends. The two types of data that you will want to become familiar with is operational data and decision support data. a single transaction may be comprised of at least five fields. or the purchase of an order. rather than in individual areas. The purpose. it may also be more current. and these are dimensionality. you will often be required to join multiple tables.Because it deals with multiple subject areas. The operational data will be calibrated in a way that allows it to deal with transactions that are made on a daily basis. and the information stored in them doesn't change as much as it would in a common database. Data warehouses have become more important in the Information Age. decision support data is often useful. The managers within an organization will need information that is summarized at various degrees. In the relational database. Because of this. The differences between decision support data and operational data can be split into three categories. However. tables are frequently used. In most cases. As can be expected. managers are often more interested in the buying patterns of a group of customers. They are not volatile. Operational data will deal with transactions that have occured within a certain period of time. timespan. this data will be updated on a frequent basis. a record must be made of it. or current. Many data analysts are concerned with the many dimensional aspects of data. While operational data will deal mostly with transactions that are made daily. decision support data tends to deal with long time frames. These transactions will deal with things such as the inventory movement. While it may be summarized. Instead of dealing with the purchase of one customer. decision support data will give meaning to the data that is operational. and it is much different than the simple view that is often seen with operational data. and the tables must have fields. and granularity. Every time an item is sold to a customer by the company. In this situation. and they may be normalized. and they are a necessity for many large corporations. it is not conducive to queries. To ensure the efficiency of the system.

because it will give you a good idea of how you should approach a data warehousing project. and they can find connections in data that cannot be readily found within most databases. You have the idea that can allow the company to improve. Once a company or organization has followed all the steps above. a number of approaches have been devised which make using the data warehouse much easier and efficient. The information should be detailed.
Creating an Efficient Process for Data Warehouses
Since data warehouses were first introduced during the 1990s. you should be able to determine if your business is performing good or bad. any problems you're experiencing at this time can be solved. The first thing that an executive will want to decide with this model is how their business is doing. Once you have established why your business is performing the way it is. you should know about the factors that have caused this. you will next want to deal with the "what if?" This is important. If the business is conducted differently. The MDMP is a spherical model that is based on cause and effect. If your business is not doing so well. The tool that you use should allow you to get to the detailed data that will allow you to see what caused the success or failure. and high classification for various investments dependent on their risk. The MDMP is excellent for those who approach data warehouses from a business stand point. it is possible to generate a memory of various initiatives that you have tried. you will now want to invest. One approach is called the MDMP. and the business manager can be given the necessary capital for the investment. along with the
. It can be fully broken down into four fundamental parts. Once you have completed this stage. Within the data warehouse. This is an important question. you will next want to decide why the business is performing this way. One of the best ways to approach this issue with your data warehouse is to set up a low. medium.They are much more elaborate than a mere database. and you will need to have the data summarized so that you can analyze it with simple tools. a large number of companies have failed when attempting to implement and use them. or Management Decision Making Process system. It could be said that the early years of data warehousing was filled with trial and error. but rather the policies and the processes that the company used when trying to implement and utilize it. If your business is doing well. Today. because it can set the stage which will allow you to make fundamental changes to your business. you should know about the factors that have caused this as well. Once you have established this. The goal of the MDMP is to simplify the task of dealing with the data warehouse while reaching core goals.. In other words. they will want to again ask themselves how their organization is performing. One of the most important characteristics of a data warehouse is its ability to give executives a type of forecast on how certain business moves will pan out. Many of these failures are not a result of the data warehouse itself.

as well as their environment. You cannot lose sight of this fact. this can become very beneficial for the company over the long term. The reason for this is because getting higher levels of quality is costly. and look past all the bells and whistles. This products were produced by technical individuals who paid more attention to function than the actual goals of the product. keep it simple. Quality can simply be defined as reaching the expectations of your customers without going above them. it is also important for companies to learn when quality needs to be emphasized before the actual data warehouse is built. With a product measure. Companies will want to measure things such as "activities" and "lengths. the technical details of it can become exceptionally complicated." Measuring a process is much different than measuring a product. Don't lose sight of the primary goals of the data warehouse. One of the reasons for this is because data warehouse processes have not been well defined up until today. Quality should not be defined in terms of data. and this is precisely why they fail. Many companies lose sight of the simple premise. it is process based measurments that should be used. By using the simple model that is discussed in this article. If a company doesn't take measurements. Because data warehouses are a process.
Understanding Quality Management For Data Warehouses
Quality is an important concept when it comes to data warehouses. Some business executive may want to know why taking measurements is so important. While the basic premise of data warehousing may sound simple. Remember. along with making the necessary adjustments for the actual measurement of the data. When I talk about quality in this article. and a data warehouse much be approached from a process oriented perspective. In other words. you will measure things such as the volume of the data you have. In other words. Many people have asked themselves why data warehouses are so complicated. and even if you surpass the expectations of your customers. As you can imagine. In addition to this. a company can save itself a great deal of headaches and financial losses. I'am referring to the success rate of the data warehouse in conjunction with its ability to help the company achieve its goals. there is no guarantee that you will have a higher rate of return. A company that wants to succeed must measure what they've already done. everything they perceive will be highly subjective. the goal of a data warehouse is to help management make better business decisions. While getting quality in your data warehouse will not be free. the costs will be much lower than having a data warehouse with poor quality. This is why data warehouses are referred to as a process rather than just a technology or a product. This is what will allow a company to succeed withn implementing this tool. I'm talking about the big picture. The costs that you will have to pay for quality will come in
. a company won't know if they are continuing to improve over time. even though having quality data is important.results of these initiatives.

If they cannot. You have a greater knowledge of the needs of your company. It is also important to realize that data warehouses are tools that must evolve. and technical success. Once the problems of today are solved. It could be argued that re-planning is the most important factor in data warehouse quality. Some experts feel that data warehouses is a process of evolution. A data warehouse is not one technology. Companies that want to produce quality management for their data warehouses must know what they have done right. and measurments.the form of re-planning. When the data warehouse increases the bottom line. It is multiple technologies combined. It is also important to analyze the value of data warehousing from the business perspective. They think that by simply using the most cutting edge technology. the purpose of using a data warehouse is clear: to gain a powerful insight into decisions they can make to help their company become more productive. Metadata can play an important role in the measurement and quality of your data warehouse. the true measurement of a data warehouse is whether or not the data warehouse can help the business succeed. and company must be prepared to deal with the problems that will occur tomorrow. Over time. as well as what they have done wrong. The first step in successfully evaluating software for your data warehouse is to do the analysis yourself. and this is political. and this knowledge is superior to an entity that lies outside your
.. You should never rely on someone who is not a part of your organization. the upper management in the company must be able to see progress. they will automatically be given an edge in the marketplace. the company has succeeded technologically. This is precisely why they are often built in an incremental format. Based on this. For business people.
How To Evaluate The Software For your Data Warehouse
When a company evaluates software to use in conjunction with their data warehouse. This is where metadata becomes so useful. When the right tools have been chosen for the right tasks. It is attitudes that like that often cause data warehouse projects to become failures. economic. and they also feel that companies need large scale projects that can be built in three months rather than three years. the data warehouse project will be considered a failure. Most importantly. and once a company purchases it. Many companies make the mistake of believing that a data warehouse is silver bullet. it will need to be customized. a company has succeeded economically. implementation. When the company is using the data warehouse daily. There are three types of success that companies must aim for. Every technology that you come across will need to be evaluated carefully in order to be of great benefit to you and your organization. they goal should be purchase software which falls under the best of breed category. the company will need to establish guidelines for operating the data warehouse if they wish to run the program efficiently. it has succeeded politically.

A number of high quality data warehouses systems can be successfully built without having to purchase outside tools. The only thing that outsiders should be used for is their knowledge of the things that you can use to evaluate the product. because it allows you to get practical information about the software you're evaluating. You should also get references from reputable sources that can give you an idea of the quality of the product. it is much better to use existing technology rather than spend money on new software. they may urge you to purchase it without carefully evaluating rather or not it is truly the product your company needs. If possible. Making comparisons between them will allow you to find advantages and disadvantages among them.
. When you want to see the data warehouse software in action. Another important tip of finding good software for your data warehouse is to decide whether or not you have technology within the company that can perform the task. This is very important. When you talk to the reference sources. because it will allow you to make comparisons between the software products. as well as the industry as a whole. Getting references is important. the vendor will often require you to look at a demo. Many of the reference sources you will encounter have spent a great deal of time evaluating the software. because you don't want to waste money on something you don't need. Any time your purchase new technology. as many of these individuals are paid by the software vendors to praise the benefits of using their software packages. If they have a vested (financial) interest in a specific product. It should also be noted that most vendors will not want to do this unless you are interested in spending a great deal of money. software that you workers will need to learn how to use. you can afford to place a high emphasis on the information provided by the software vendor.organization. Another problem with dealing with outsiders is that they may have biases. This information can allow you to make intelligent decisions on whether or not a software product is worth your money. you will often here of some pundits talking about the benefits of using certain data warehouse products. When you read some technology magazines. One good way to evaluate a software program is to analyze the stock of the company. and they will tell you about problems that the vendor would "forget" to mention. Many business executives find that there are a number of operational issues that they find when they begin seeking references. it will place a type of burden on your workers in which they are forced to deal with it. My advice to you is to be cautious in their endorsements. One could way to evaluate this software is to create a test case. This is crucially important. you will want to ask them for websites or other places where you can find useful information on the product. and have each vendor follow it. Because of the costs involved with data warehouse software.

Data warehouses are much more complex than OLTP systems. it will fail. and this is a grave mistake.This will give you an unbiased view of the company's performance. If the data warehouse does not have the support of the employees. It is also important for companies to realize that data warehouses are not core business tools. Some experts have even said that data warehouses are one of most overrated tools in the computer industry. To face the challenge of implementing a data warehouse. Being able to afford a data warehouse is just one of the many requirements a company will want to look at when implementing them. While they are great for the companies that properly implement them. many of their developers are not trained in calibrating them. Many companies compare data warehouses to standard OLTP systems. Many employees have a hard time using data warehouses because of their complexity. Find out what will happen if the fields in the data dictionary are changed. Many of the companies that implement data warehouses are disappointed with its performance. and your company must be prepared. Many companies decide to use data warehouses beause they simple think that it is the "next big thing. it is first important for you to understand why they can be challenging. To make matters worse. there are many challenges that a company will face in their implementation and utilization. The software maintenance is also very important. they are disasters for companies who are not prepared. Any company who thinks they can "have their cake and eat it to" are in for a rude awakening. It is also challenging for companies to keep their data warehouses in tune with their production units. Each one will have its own trade offs. First. A large number of these companies fall victim to vendors that promise to help them easily implement and use the product. and these tools are further broken down into many categories. Many data warehousing tools will work with a device called a data dictionary. One thing that should be emphasized is that there is no such thing as a perfect software product. Much of the literature that is written on data warehouses is to positive
. and the companies they work for will often make the situation worse by failing to educate them. many of these vendors are more concerned with turning a profit than helping the company succeed.
Understanding The Challenges of Using Data Warehouses
While data warehouses can be greatly beneficial to the companies that use them. It should be noted that data warehouse projects fail frequently. constructing a data warehouse is much different than constructing an OLTP system. There are a large number of tools in the standard warehouse. What this means is that a data warehouse is much more vulernable to the politics that may occur within a company or organization. However." and they don't take the time to think about the requirements they will need to meet in order to use this tools.

It is also important to make sure your employees accept and understand the use of the data warehouse. Unless they are a good company. However. a company sets themselves up for failure. If the user does not understand basic SQL. since it plays an important role in the construction of many data warehouses. Don't rely on the vendors to tell you about this. a company must be aware of the pitfalls involved with using a data warehouse. and they don't spend enough time talking about the negatives of using a data warehouses. because they will be too busy trying to sell your company the product. and the careers of some of their employees may be ruined. it will be difficult for them to use the product efficiently. Is the vendor reputable? Do they have a history of helping companies successfully implement their data warehouses? How long have they been in the business? It is amazing to see so many companies fail at their data warehouse implementation because they were distracted by the bells and whistles of a product. your success implementation of the product is of little interest to them.toward the topic. They need to be given an elaborate education on using the program.
. By not doing a detailed requirements analysis. It is best to avoid doing business with vendors that don't have a proven history of success in implementing a data warehouse. If a company wants its workers to use the data warehouse. The biggest mistake that a company can make is not properly analyzing a data warehouse project before paying for it. and this should play an important role in the implementation of the data warehouse. They will waste time. they must become familiar with fundamental SQL. money.
It is also important for the company to pay attention to the vendor who is selling them the product. This article is not meant to scare a company away from implementing a data warehouse.